Welcome to part two of our three-episode limited series with the one and only Dr. Stephen Seiler.
It wasn’t that many years ago when if you said you were part of a physical education program, it meant that you taught kids how to play dodgeball. Exercise science as an area of education and research is something that came into existence during the lifetimes of many who study it now.
So, it’s both surprising and encouraging to see how science has now become central to athlete training at all levels. But many athletes who try to apply a scientific approach to their training are often left with as many questions as answers. More importantly, they wonder about the gaps in some of the key aspects of training.
In this episode of The Stephen Seiler Podcast, Dr. Seiler tackles the field of exercise science head on. He starts with the 100-year history of exercise science research. He then gives those of us who don’t work in academia a peak into the ups and downs of doing training research, including why funding can be so difficult, the challenges of some specialties, and how crowdsourcing could revolutionize the field.
So, settle in for a history lesson with insider access from Dr. Seiler, and let’s make you fast!
RELATED: Episode One of the Stephen Seiler Podcast: Dr. Seiler and the Heart
Episode Transcript
Rob Pickels 00:04
Welcome to part two of our three episode limited series with the one and only Dr. Steven Seiler. It wasn’t that many years ago where if you said you were part of a physical education program, it meant that you taught kids how to play dodgeball. Exercise science as an area of education and research is something that came into existence during the lifetimes of many of us who now study it. So it’s both surprising and encouraging to see how science has become more essential to athletes training at all levels. But, many athletes who try to apply a scientific approach to their training are often left wondering why the science says what it does, or more importantly, why there are gaps when it comes to some key aspects of training. In this episode of The Dr. Stephen Seiler podcast, Dr. Seiler takes the field of Exercise Science head on. He starts with 100 year history of exercise science research, then gives those of us who don’t work in academia, a peek into the ups and downs of doing training research: including why funding can be so difficult, the challenges presented by many topics, and how crowdsourcing could revolutionize the field. So settle in for a history lesson with insider access from Dr. Seiler, and let’s make you fast.
Trevor Connor 01:20
Joe Friel, Dr. Stephen Seiler, Julie Young, Julie Dibbens, Jim Miller, the list goes on! Fast Talk Laboratories is becoming the home for world class coaches and training science experts. Now you can get it all free for 30 days. Join now at fasttalklabs.com with the discount code 30 days free. That’s THREE ZERO DAY FREE, and you’ll unlock all our content. Our trial offer expires October 31. So join now to get all the latest content from your favorite experts.
Dr. Stephen Seiler 01:57
Hi, this is Dr. Stephen Seiler again, and today’s an interesting day because I guess I’m gonna start with that first word in my name or the title Doctor. Because what am I a doctor of I can’t prescribe medicine or anything useful like that. But I am a doctor in something called Exercise Physiology, or sport science or exercise science or Kinesiology. It has a bunch of names. My particular field was physiology, exercise physiology, and biochemistry. But I come from a an academic tradition that hopefully is useful to a lot of the listeners out there in the sense that we try to do research that can answer questions about the training process. And today, I’m going to talk a bit about Yeah, a little bit about our history how this whole field got started. But more importantly, to get to the point of where are we now and where are we going because technology, the digital landscape we work in and so forth has really dramatically changed the way we can do some science and answer some questions about the training process about what happens in competitions, many different things. And I think probably what has spurred some of this on at an even faster rate is, let’s face it, COVID-19. This last year, this last eight, nine months, where we have been pushed out of laboratories and so forth, has accelerated, let’s say some processes around using these digital tools. So that’s what I want to talk about today. Now, just before we started, I was talking with Chris case, who’s sitting quietly right now in the background, and I was trying to we were trying to decide what his role is here. And you guys know Chris from fast talk and you know, he’s super fit and very well read. But in this scenario, he’s going to be like the Ed McMahon to Johnny Carson. He told me
Chris Case 04:11
if I could only do my best Ed McMahon right now. I can’t even think of his he’s got a bit of a gruff voice. And his build is definitely not anything like mine. I don’t think he would call him in shape.
Dr. Stephen Seiler 04:23
He weighs twice what you weighed twice what you do.
Chris Case 04:26
So you’re probably right.
Dr. Stephen Seiler 04:30
So in kilograms, he’s twice the man you are but as a producer as a as a person who can help us in this conversation. He cannot even hold a candle to you, Chris.
Chris Case 04:43
Thank you for that.
Dr. Stephen Seiler 04:44
So you’re in the background just so our listeners know. Now, if you go back to the start, look, I got I got started this in 1983. I was 17 years old. I discovered that science and sports possibly could live together, because in my youth, I had seen them as two completely different things. I had a laboratory under the stairs and as a little kid where I did the science geeky stuff, and then I played all the sports. And then I suddenly discovered that these things could come together, thanks to some early days articles that were written. And anyway, so I jumped on. And I started a bachelor’s degree in something called Exercise Science at the University of Arkansas, and that program was new. And it was part of the Health, Physical Education Recreation and Dance Department, which is a very typical combination. They even call it hybrid, the give it an acronym, but it comes usually a lot of sports science programs that developed in the United States where I studied, came out of teacher education, out of physical education. And then, you know, some came out of medical programs, but most came out of that tradition of the of the teacher education program. And so my degree was one of the very first that didn’t have I was, I didn’t get any kind of teacher qualifications, I got a pure Exercise Science qualification that maybe would have given me a job as a test technician or working in a fitness, or a rehabilitation center, cardiac rehab or something like that. So this is kind of where that developed. Now, if we go over to Europe, Europe has some more typical schools of sports science, you know, the Norwegian School of sports science, you have the the German school of sport, you have a little bit different organization, and that maybe has had some impact on how research has worked in the questions that we work with. But one thing that has definitely influenced the research process in this field, you know, and if we focus in on, you know, if you say, Well, I want good research on endurance training, why don’t use I see more of that? Well, the answer to that question, folks, is that money talks in in academia, we end up chasing the money, especially, I would say, in the United States academic system. It’s true in Europe as well, but it is developed at an accelerated pace. Having to chase X, what we call external funding, became basically your source of existence. And so in these departments of Exercise Science, what ended up happening is that the money was related to health, you find the money from the National Institutes of Health from the American Heart Association, and so forth, if you’re in the United States, and also in many countries in Europe, taxpayers pay to solve big public issues. And winning gold medals is not a terribly important public issue for most countries. So the money is often connected to dealing with the obesity epidemic with diabetes, and so forth. And then if you’re lucky, you have a grant, as they’re called, to do a big study related to childhood obesity in the schools. And because of that, you have some infrastructure that everyone’s wanted, you can also do some fun stuff, like a training study on cyclists or something like that. So that’s often how these sports related projects actually get funded. It’s kind of the we call it piggybacking on top of a bigger source or a main project that is funded in your laboratory. So if we look at the list of top universities in sport, so called sport science, and really drill down and try to see who’s doing this stuff that’s related to performance, well, there’s not very much of it happening, relatively speaking anywhere, but particularly not happening in the United States. And in places where the pressure to get this external funding is super high. Because there’s just not much money for it. There’s not much money connected to sports performance. It’s easier to do nutrition related studies, because you can find companies that will fund your research in hopes of that it will be the results will be something they can use and you know places you know, Gatorade, Nestle, there’s been lots of big companies that have funded a lot of nutrition research related to sports and recovery and so forth. Dehydration, you name it. So that’s been an easier pipeline to get connected to then so once and I want to have a new interval training program and I want to test out, because there’s really no way to sell that new interval training program and make a bunch of money. Anyway, that’s that’s kind of part of the growth process, or part of the background is just what how have these programs evolved? If we go way back to the very, I would almost say the start. Some of the institutions that were really had a huge impact on the field over many decades. But one of them was Harvard, but not, not the way you would have guessed, because Harvard, back in the early or late 20s, established something called the Harvard fatigue laboratory. Now try to guess, which department or faculty at Harvard was the host and funded this new laboratory related to human work and fatigue? It wasn’t any kind of physical education department, it wasn’t the Faculty of Medicine. It was the business school. Now, why in the world did they do that? Well, this was a time when there was a lot of automation or factories where people were working on factory lines. And they were interested in effectiveness of how to optimize the or increase the work effectiveness of people in these factories doing these different menial jobs. They were interested in lighting and temperature and how many breaks to give them and, and they were basically the people working in factories were treated, essentially, as, you know, just sources of work production. And so the Harvard fatigue lab, I have to say was, was built on a business model. It was designed to improve business efficiency, but they ended up doing an amazing amount of really good work. They only lasted 20 years, World War Two came in and when World War Two came another thing happened often happens in exercise science. And Matt is that the military said, Hey, we have some questions we need answered and we have money. And then the researcher said, Whoa, yes, sir. We’re What do you want us to do and how much money and of course the questions in the military are often related to dealing with environmental threats, like, you know, chemical warfare, heat, whatever it might be in the theater of that they’re going into to perform these, you know, the role of, of the military, the physically demanding things they do. So there was a lot of military research that got funded in the harbor fatigue lab, did a lot of that. And then after the war was over, the money dried up and pretty quickly by already by 1947. After 20 years, they got disbanded. Another great program that we can connect to those of us who are interested in endurance would be Carlene skor Institute in Stockholm, and the gymnastic school in kimberleigh State College, at the same place in Sweden, and they ended up doing some wonderful research on the basics that taught us about the maximum oxygen consumption and its role in endurance performance. If any of you have been in a laboratory back in the 70s 80s 90s. Even now, you may have gotten on a Monarch training or garment or or bicycle or Garmin, or it was the testing bicycle for decades. And it was it came out of that same environment, it was kind of a innovation that developed where a company said, Hi, I guess we could maybe take our usual bicycles and figure out a way to use them to actually measure power or work rate. So these kinds of things happened in this was in the 50s 60s, and so forth. And then if you come bring it to the 70s, and 80s, people like me come into the program, and some of us we can even trace our lineage via our different advisors back to either Harvard fatigue lab or, or lean skill or some, you know, a few of these seminal institutions that kind of were the early first responders or starters of these kinds of programs in something like exercise science. And now of course, there are hundreds I checked in on the Shanghai rankings. There are three about 350 Different institutions around the world that offer some kind of a sports science degree or education. So it is spread throughout. And that’s the background. And now what kind of research was going on? Well, basically, you you either do research on physical education, you know, the teaching process or It research related to some kind of a medical question, like I was saying earlier, or you may have been able to move down a path towards studying a training process, strength training, endurance training different, you know, different aspects. And of course, sport science is more than physiology. It’s also psychology. It’s biomechanics, its nutrition. So it’s one of these science that is actually loaned or borrowed from many other fields, chemistry, anatomy, you know, you could name it a list of 10 or 12 different fields where we borrowed from them to kind of create some hybrid or accumulated field that we just call sports science. So this kind of emerged, and I’m down that physiology path. But to really do good studies on the development in the training process, you certainly need to, you do need to crossover and a good department has, they have bowel mechanism, physiologist, and psychologists and so forth. Hopefully, work together not always doesn’t always happen very well. But that’s that should be the goal. At any rate, I did my education in the 80s. And in the early 90s, people like me that were doing physiology, we had two choices. You either used rats, or you used humans in a laboratory environment. So you had to either take care of your ads and bring your rats into the lab, or you had people that you could recruit, often students may be some well trained cyclists in the community or some runners, and you would bring them into the laboratory. And the laboratory was the domain that where we could do anything related to understanding the training process, we had to have control and an instruments of testing and measurement were, you know, they were often one off, meaning that you couldn’t just go out and buy one, you had to build it or you know, put together the analyzers, or the oxygen sensor plus the co2 sensor plus measuring the ventilation and you put together a system and you can measure oxygen consumption, for example. So these labs were kind of mechanic shops where you had to be resourceful, you had to build your own equipment, get it to work together, you are constantly tinkering. And some of the best scientists were also some of the best thinkers. And that’s how they built up these laboratories. And then slowly some of this stuff, you know, there was enough demand that you started seeing companies that said, Well, we will build, you know, the VO two systems and so forth. And that that got easier, it got easier to do the testing. If you go way back to the 50s. And you look at a vo two max test, it actually may have taken several days to do 40s 50s Because they didn’t have automated systems. So they would have to do a five minute period at a certain speed and then collect the gas. And then on after they would have to then analyze that gas, very painstaking leave the chemical composition, how many liters did they breathe in and out and do all the math to say, well, that five minute period at 250 Watts, this was the VO two and then they go to the next step. So it could take a long time to get an actual what we would call a vo two max test, which we can now do in minutes. So the the work that was being done back then was painstaking, it took a lot of time, the accuracy was very good, to be honest, but they certainly could not measure a lot of people. So the early studies, if you go to the textbook of work physiology, which has some of the classic stuff on interval training, it’s based on one person or two people that were measured. Now the data is good this day today, but it’s not a you know, it’s not 20 people or you know, a big group, because those were really difficult to achieve, both because of recruitment and also just because of the the technology that they had at the time, we go a little farther into the 80s and 90s, then, you know, the equipment gets better. And so a lot of the research studies would be say, a group of six or eight, you know, or maybe 10 subjects that would all do the same thing. And we would look at lactate threshold or or some, you know, learning some of the basics about how their body was responding to different kinds of training and so forth. So, you know, lab studies were painstaking, but we had good control and the literature is full of studies with very, very small sample sizes. So that’s been the trade off is good control in the laboratory, but generally, fairly small groups that you’re able to get through a eight week training study, or whatever it might be, you’re limited to in numbers. And when you have small studies, then there’s lots there is variation. And so it’s it gets pretty hard to be very convincing about any of your results, especially when you’re comparing different ways to train because you, we know that there’s individual variation in response, and so forth. So it doesn’t take very, then take more than one, we might call outlier. Somebody that responds unusually, before, there’s really no clear result in these studies. So that’s kind of the sport science world has been fraught by what we call underpowered research studies. And it’s not the fault of the researchers, it’s just it has been the nature of the game, the nature of the situation, we work in the nature of the facilities, the nature of the type of research you’re trying to do. Now, we, we sometimes use rats or animal models, to get at more mechanistic things I do that myself, I used a rat model where I took the hearts out of the animal under anesthesia and, and actually tried to look at some mechanisms about heart performance under very, obviously very artificial and controlled conditions. So animal models, obviously have their advantages in terms of being able to be very mechanistic, being able to say, X causes Y. But then translating x and y to humans and even more to human athletes, obviously, is a huge leap. And it’s not usually a leap that is seen as valid, necessarily. So that’s also a bit of a limitation. I went back to humans as a as a sport scientists in haven’t touched a rat since 1995. Myself, but the training I got, I felt it was valuable for me, because it taught me about some cellular mechanisms and so forth. Do you know they are similar in the human body? Oh, where are we? Well, in the, let’s say, as I was finishing up my PhD, along comes the internet, back around 93. And as we know, most of you, some of you have never even maybe even lived in a world or it can remember not having access to this digital landscape. And it’s become evolved more evolved and, and so now, some things have happened that have potentially just really changed the research process in related to, particularly our field because some of the changes, some of the possibilities that are now there, in terms of technology are phenomenal. To be honest, I find it just really exciting. Even though I’m 30 years into my career, I see that I think Man, this is just a whole new ballgame for me to try to understand things that I’m interested in, like the training process. And if I sum it all up, I would say it all adds up to us that we’ve got these so called crowd source approaches that we can we can pursue now that we can use, if we start at the kind of the most basic, we can use public databases that have emerged over the last 20 years. One of the first ones that I got interested in was the concept to rowing data that it was they you know, they call it the world rankings. And it was the concept two rowing machine, which was developed by the Dr. Isaac EC are brothers up in Vermont, and it became kind of the world standard rowing machine accepted as being valid that you know, the machines were reproducible, they gave reproducible data and they started having competitions on these things. They started having these so called you know, World Indoor Championships and a lot mostly it was just you know, the the rowers from the various national teams and from the clubs and that they would, would compete on these on these pain machines. But there was data that was being generated. They were We were suddenly getting data on the at the watts that were produced. The power that was produced in concept two was pretty early with making this data publicly available on a webpage they had they made the rankings available for women for men for heavyweights for lightweights and then pretty quickly they were they had age groups. And so before long they’re starting to develop a day database that is potentially telling us something about the aging process about gender differences in you know many things. And then they had different distances, they added 500 meter 1000 meter 2000 10,000. So then you’re starting to generate data that says something about the power duration curve. So even as a PhD student back in the 90s, I discovered this and ended up it was I ended up managing to get data from them and create it do a publication on a gene in this power curve, you know, the power duration curve, and so forth. So that was my first exposure to this crowd sourced approach. But it was much more painstaking, back then it was, you know, a big Excel file, and I had to clean it up and so forth. But it was a start. And since then, of course, you have like the IWF. World, athletics has data, going back now, a good 20 years, on all of the different events, top, I think now, that’s the top 500. It used to be I think about the top 200 performances each year in each event, every track and field event, plus some of the big street distances, you know, the half marathon and so forth. And that data has now become available. And now we have intrepid data types that can go in, even though you know, they’re not part of World athletics or IWF, they can do something that my friend John Peters just calls scraping, they can scrape all of that data from that public database and then feed it into their own analytics platform that they purpose bill. That’s something that I did with John Peters, or he did the coding and I did a bit of the the science geek part on the on the rolling data for concept to John Peters develop the same thing for the IWF are now it’s called World athletics data. Another example is the power of 10 Database in the UK, and in some countries have national age group competition data. For example, in Norway, I have colleagues that have found they have all the age group data from different from track and field and I can say something about aging and performance, not aging, but development. And you know, questions like are the best juniors also the best seniors? Do they tend to also become the best senior performers or do they fall away? And so we have, we’re able to answer these questions just from publicly available data.
Rob Pickels 27:44
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Chris Case 28:28
Dr. Sylar, when you say data, what exactly do you mean? Because in the cycling world, I think people hear data and they think power data, heart rate data, things like that. Are you talking more in this instance, just purely times over distance for a given track event?
Dr. Stephen Seiler 28:47
Right. So at the very beginning, the first database is word just times, distances, watts, you know, the concept to it was times but we were able to convert it to watts, because it was a, just an algorithm. So that’s what the first database is. We’re we’re just performance data. So that’s a good clarification. Now, the next level that has happened and I guess you’d have to give maybe training peaks credit for being if not the first, one of the very first players in this was this idea of a an online training diary, or a training database if you want. So training peaks came along. I can’t remember what year they first started, but it was after the internet started and it was probably somewhere late 90s that they got into the game and said look, we can you know the Internet is a powerful tool, and we can create software that coaches can use. Well, if we fast forward to today, or if I go to least December 218, which is the last time I was actually set been having dinner with Dirk Friel. And he said, Look, we get in 10 million training sessions per month into the training peaks data base. So that’s a tremendous amount of data. Now, the quality of each of those files is obviously very different. Some of them our power, plus heart rate plus descriptions of how they felt that day, but you know, lots of information, whereas so maybe minimalistic, but training peaks with their data analysts that he can filter and smoothen and identify different subsets of people within the data that had, for example, one time, they said, Well, we’re just going to find all the people that ran the Boston Marathon. So they just use GPS data, and the data to figure out where they were where they actually running on the Boston marathon course. And then they could identify several 1000 that did that also use training peaks, and then I can go in and look at their training characteristics retrospectively, relative to the performance time they had. So so these are the kinds of things that now are, can be done with these big databases. Now, the downside is, is they’re, they’re proprietary. So you have you have databases, like training peaks, you have Strava, you have, if we go over to just pure cycling, you have like trainer road, I think suffer fest is primarily cycling. So you have these different proprietary databases, but groups, teams coaches, they can use the training peaks platform as a tool to collect data on their subjects, their athletes, their team, and then connect it to other data they may be collecting. So these programs have really enhanced in many ways, I would say they’ve enhanced the quality of some of the research that we do, because we had better data on what the subjects or the athletes actually did. Training was. And that was one of the weaknesses earlier was we were really good at measuring whatever they did in the laboratory. But we weren’t necessarily very good at actually measuring the training that we had told them to do, did they actually do what we said. So that was a bigger problem before but now we’ve got this digital footprint of essentially everything the athlete does, because as you guys know, listening that most of you will say that if it isn’t on one of these Strava or whatever, then it didn’t happen. So you’re pretty good at always making sure your training data goes into one of these digital platforms for your own site, so that that’s been a an improvement for for us that are interested in training studies, or doing training studies, and so forth. But it’s proprietary, you can’t always get at it. Strava does it, you know, they’re pretty tight with their information. But but some of this is starting to open up, for example, the program trainer road, I happen to be working with a PhD candidate from the University of Toronto, and he is going he’s doing a project where TrainerRoad is going to serve as the platform for a training project. So that the subjects hopefully they hope to maybe have 1000 subjects or more in that study. Now if they do that, that’s a game changer. If you can imagine having a research protocol where you’re actually having people go into different groups, doing different types of training, and it’s been controlled, you know, and you can have 1000 subjects. Well, that is a game changer. But, you know, the detractors will say Well, yeah, but you don’t have lab control, they’re using different or Gardner’s, you know, different different bikes, different power meters and things. And yes, that’s, that’s a weakness that in, we won’t have that perfect lap control. But on the other hand, we have potentially a tremendous increase in the number of subjects and and that tends to overwhelm some of the noise in the data. So this is kind of, I would say, one of the key things that is happening in sports science is that, at least in some areas, we can use these crowd sourced approaches. I can recruit people via Twitter, and I can get quickly 100 people to do a very specific training protocol that’s fairly well controlled, and I can give them fairly explicit instructions regarding what the before and and to weigh themselves prior and all of these things, just like I would do in a laboratory, but now they’re each on their respective trainers. Some are using attacks and some are using a wahoo kicker and some Using this and then, and so there’s gonna be some plus minus on the on the accuracy of the Watts. But for the individual, that comparison will be with the same trainer with the same regardless. So 200 Watts, that goes up to 225. For that individual, that’s going to be a comparison I’m comfortable with, as the scientist, I’m not going to necessarily compare too much across people, at least not down at a very detail level. But this is where we are moving in is that we can, we can have lab studies that are mechanistically oriented, that we’re very, you know, we measure more variables, we can measure things that we can’t measure out in the field. But then we have a crowdsourced arm of the study, or a part of the study where we have big numbers. And it’s, you know, it’s real world, it’s how things are actually happening, because people are training, either outside or inside, in their garage, or in their kitchen or wherever. It’s more realistic, actually. So that is an exciting area. And I would say it was pretty interesting. During March, April, May, particularly when we had the big shut down, lots of people were kind of forced indoors. And at least in my case, I experimented with the idea of trying to organize a study, not a training study, but a comparative study looking at some different workouts. And it worked, I was able to recruit about 50 subjects, and most of them executed the protocol as they were supposed to. And the data is actually now part of a manuscript that’s now sent in for evaluation. So that was one of the first times that I’ve been involved in a study like that. And it’s kind of exciting, I think, I hope I will do more of those. And I know a lot of different people are looking at this approach. As an example, you know, if you take Marco Latini, who’s developed the H HRV, for.com, and he, you know, has the his heart rate variability tool, he also collects a lot of data. And he is actually he’s published a number of studies, using the users of his platform, as a subject population, you know, and looking at their training characteristics, the relationship between their training and heart rate variability on a big scale with 2000 subjects. So that’s something that will, you just wouldn’t be able to do in a laboratory. But he’s able to do it through his commercial platform. And he’s an engineer, biomedical engineer, I would say, by a trader, you know, with a great data analytics training. And so he’s done some really interesting stuff. And that’s just one example of, you know, different ways that I think we’re going to see the endurance training process and different kinds of training, studied, optimized. And maybe we’re going to be able to go from generalities to very specific individualization keys, and how can we get at that individualization process. And to do that you got to have a lot of, you got to have big numbers, you can’t have a group of eight subjects in a laboratory to look at individualization you need, you really need hundreds and even 1000s, to look at the details.
Chris Case 38:23
Question for you sort of a philosophical question, I guess. But why do you think people out there in the world are interested in doing this for you, or others that are wanting to crowdsource the data, just being a part of something within science is reward enough for you they are getting something in return?
Dr. Stephen Seiler 38:43
Yeah, I think that’s a great question. And I think the the answer, I hope, is that they perceive that they get something back. And that’s always been my concern, as a sports scientist is that I, you know, I don’t want to miss you subjects. I want them to learn something that they can take with them into their training in the process. So in general, the feedback I’ve gotten, at least from my stuff in and I’m fairly active on Twitter and so forth, is that, hey, you disseminate a lot of stuff, or to me, to us, I’m happy to give back in the form of sitting on the bike for two hours and doing this project you asked me to do. So it’s kind of a quid pro quo. I would say. Now, there are just some of them are just sciency geeks that love this stuff anyway. But I think also, that’s part of this is a relationship that you try to build or I try. I think that I’m not alone. But as a sports scientist, I know that this stuff is not. It’s not easy. So I’m asking them to sweat for science. And in return, I’m going to try to give them something back in the form of actionable information. So anyway, that I think that It’s part of that relationship building. It’s different maybe today than it would have been 2030 years ago where there was more kind of distance between the scientist and the subjects, if that makes sense. So I don’t know if that answers your question. But that’s what I see, at least now is that even though we’ve never met each other, we have a dialogue going on through these various social media platforms.
Chris Case 40:24
Right. Now, that makes a lot of sense. When I was working at the National Institutes of Health, we also as under paid fellows would seek out studies that we could participate in as subjects. And, of course, the more invasive they were, the more you got paid in cash. So we would seek them out. But yeah, there’s no opportunity for the exchange of, of money here. So the Yeah, just curious to know the reward. And that answer corresponds with what I anticipated, you would say. So yeah, thank you.
Dr. Stephen Seiler 40:59
Yeah. And it’s very different from biomedical research, like you’re describing, in my early days, my first year as a PhD student, I can still remember me and a couple of others, we decided that, you know, we were poor, and man, our stipend just was not going to give us much over our daily bread. So we, we decided that we were going to be subjects in one of these biomedical studies. And there was this huge pharma, biomedical place where they did all this stuff not far from of campus. And so we just all went over there, three or four, there was three of us. And we were going to be in one of these studies, and all three of us got kicked out, we were not qualified. And it was because all three of us were not normal enough.
Chris Case 41:45
I understand
Dr. Stephen Seiler 41:46
we were not couch potato ish enough, that we could go into the study, because my resting heart rate was too low. And I think both the other guys did quite a bit of string trading. So they had some liver enzyme issues. And so that that cash cow that we thought we were going to tap into just never materialized. For us, in such as the case in terms of training studies, money’s never on the table. So I think it’s always been the subjects you bring in, even back in the day bringing people into the lab, it was just they were athletes, or they were interested, or they were physical education, students that basically didn’t have a choice. So we got to remember that a lot of it was there in this course, and kind of almost a condition for getting a decent grade on the course was being a subject in some study. And I’m not going to go too deep into that, whether this was forced labor, or whatever. But it’s been true at universities and psychology departments and sociology departments and everything for years is that the students, basically as a condition to pass the course they have to participate in X number of studies, right. And so, so that was part of the history. But then if you brought athletes in, they’re doing it because they think and they hope that they’re gonna get something out of it, they’re gonna at least find out what their vo two Max is, what’s their max heart rate, get some lactate, profile information, things like that, that they, in theory should be able to use in their training, I think today, we’re even more conscious about trying to give him something back and make sure that we’re forging a relationship. So that’s at least how we can get some stuff done. In a field where there are not big pots of money waiting for you to apply for, like there would be in, say, cancer research or obesity research or something like that. Does that make sense? Absolutely. So get the public databases with just just performance. Now we’ve got these proprietary tools like train or road or training peaks and so forth, that have opened up for, you know, to give us a wonderful digital footprint of training that we can analyze and quantify in different ways, we have to be a little careful, because the metrics that these different applications provide may or may not be validated up against other things. So you know, but that’s, that’s a caveat that we have to just be aware of all the time. And then, you know, now we’re moving down that third, maybe the next level, which is like I was saying, we can actually plan research projects, and do everything digitally. collect the data, either it could be research surveys, or the things I’m interested in actually taking advantage of the fact that most or at least a lot of runners and cyclists are collecting data on themselves, essentially every day, pace, power, heart rate, and so forth. And it’s in the form of these files, these fit files and different variations thereof, that can be pulled into an application and these applications can speak to each other through these So called API’s, you know, the, the application programming interfaces. And so we really have a lot of possibilities there. We can’t measure everything, but we can measure at least the external workloads and internal workloads, the heart rate and so forth. on a broad scale, we’re, you know, with a lot of subjects involved. So there are already studies that are being organized as we speak, that take advantage of this. And we’ll see, it’ll be interesting to see in the next year or two, what gets published, you know, does it work? Are they able, are they as successful recruiting the numbers that they think they can? Or are we overestimating the will of the people you know, the, the question you were getting at, are we overreach, overestimating the will of people to jump in this stuff and be kind of, you know, have their training programs somewhat disturbed by science geeks like Steven Siler, you know, during during the winter, so I’m optimistic, but the proof will be in the pudding in the next couple of years. Now, another study that showed up this year, during the pandemic, during the early days, when people were locked down, a group from East Asia said, well, we want to study what’s happening. And so we I ended up being part of a consortium with over 100 different sports scientists, three from Norway, but in just different groups from a lot of different countries, 35 different languages were, the survey that was was sent out was translated into 35 different languages with painstaking care and editing and work to make sure that we were getting it right. And the goal was to look at the training of what I would call nationally elite athletes. So within their respective countries, they were top performers in essentially all different sports. And so we ended up with I think it’s over 13,000 responses from I don’t even know the number of countries it is, but it’s over 50. So it’s a it’s one of the biggest studies of the training process, perhaps it’s ever been done from a survey standpoint. And this was facilitated through, you know, Google Docs and through information spreading, you know, the recruitment process was through Twitter, and Facebook and national governing bodies, and so forth. So I, I think this study, obviously, was would never have happened without the COVID-19 crisis, and exemplifies maybe what’s possible through global cooperation. And I don’t even know how many papers are going to emerge from that from that data. Because there’s just a tremendous amount of data, about training habits, you know, where you can look at different sports, you can look at gender differences, you can look at age, you can look at the level, you know, there’s going to be so much it’s going to come out of it, it’s a, it’s going to be interesting to see what happens. But that’s, you know, one of these crowd sourced approaches that are happening. So, you know, this is kind of where we’re at is, we are fumbling around trying to figure this out. Now, one of the big issues is, is security is or I should say, is the ethics. You know, if you go back, you know, to the lab studies, they are very carefully controlled with what we call an institutional review boards, where any study you do that involves humans that are subjected to any kind of risk to their health, those risks have to be carefully weighed. And this goes way back to the Helsinki declaration, I think it was from 1964. It’s been revised about 1213 times, but it’s all about medical research. And it has its roots in of course, the aftermath of things that were discovered that happened in World War Two. And so the, there has been great care to protect the safety of subjects in any kind of what we might call medically oriented research.
Dr. Stephen Seiler 49:06
So the things we do in a laboratory, obviously, there’s risk, if you’re running on a treadmill, there is at least some risk, that that person could have a heart attack or could faint or you know, or that we could infect them if we were if we draw, do a blood draw and measure lactate. So all of these things, the risks have to be weighed, and we have to demonstrate that we have thought about the risks that we have mitigated the risk as much as possible, through good procedures and things like that. And so, this is a very painstaking process. Every study has to go through these IRB reviews to make sure Well, this is a new frontier, when I am going to ask Joe Johnson in his garage, to do an interval session, you know, and, and at the same time as is going to 100 other people in 10 different countries and their age 22 to 55. And they’re telling me that they’re all healthy. But I don’t know, you know, so. So there are some issues we have a little bit, we have considerably less control. And right now, these institutional review boards are facing or seen some of these new study protocols and study applications for the first time. This is the first time we’re asking if we can, you know, Steven in Norway, if I can collect data from people from 1015 Different countries that I’ve never seen before. And I have to trust them that they are well trained, and that I’m not exposing them to a level of exertion, that’s, that’s greater than what they’re used to. So these things are, we’re dealing with so far, so good, we’re, you know, it looks like this is going to be, we’re going to be able to do this in an acceptable way, obviously, you’re not going to have super invasive procedures executed from a distance. But we will be able, I think, to get institutional review boards to approve this approach, and partly because they see that it’s necessary when labs are shut down. So that’s another aspect of this is the safety and ensuring that even even under these circumstances, if I recruit, you know, Joe from Twitter, Joe has to understand that if something happens, he can he can drop out, and I’m still going to be his friend, I’m not going to draw, I’m not going to defend him on Twitter, or whatever, you know. So that’s always the underlying issue, if you’re in a research study is you always have the right to withdraw from the study, without even given a reason. That’s your right. And so these kinds of things have to be protected in any kind of research that we do on humans. So you used to work at the NIH, you know all about this stuff, Chris? I do.
Chris Case 51:56
Yeah, this is the stuff that you know, protect subjects, but can also be not infuriating, but it slows it can slow down research. It can be I guess, you could say a hassle for scientists who think oh, this, this study should take place. There’s nothing wrong with it. But the IRB process slows it down, or there’s wrinkles in there. But yeah, the protection of the subject comes first, do you actually send people any paperwork to fill out a waiver to sign or anything to cover liability issues? When you’re seeking subjects on on Twitter?
Dr. Stephen Seiler 52:32
Well, so far, you know, it’s been pretty ad hoc. But the study we did were, you know, I asked people to ride for four hours on one day, and then another day, two hours with an six hour rest, and two more hours, we used Google Docs, they were given the full protocol, we were they were given the entire field regarding their right to know this was all voluntary, that they could withdraw at any time, a little bit about the risk. So basically, we did the same thing, they got the same information that they would have, if they had been in a laboratory, we did not extract signatures, but they had to receive this and read it to go farther. And you know, they had to agree and take the next step. And then we set up a separate email system, just for projects, I set up an email account, so that if they sent, you know, if they communicated there, they were volunteering to be in the study. And then they then received links to the different tools, the different survey instruments in the protocol, I mean, of the, you know, just for data collection. So that was, again, it was early, it’s kind of the first attempt at trying to see how do I, how do we regulate this? How do we control it as best as possible? How do we make sure that we are protecting the subjects and so forth, and in informing them of their rights to withdrawal and so forth? So, so far, you know, obviously, it went? Well, there were no, no problems. And I think there were a couple of the subjects that I just the data wasn’t good enough that there were some issues with the technical, you know, one was heart rate. And one they just didn’t do what they were told quite the way they were supposed to. But out of, you know, out of all the data I received, it was very high rate of good quality data. So I do think it gave me the feeling that there will be a new tool, a new approach that will be publishable.
Chris Case 54:36
You don’t think that this will replace the lab entirely. Do you? Next year will people be back in the lab?
Dr. Stephen Seiler 54:43
I hope I mean, we’re back in the lab in Norway under somewhat, you know, much tighter control with mask on and gloves and so forth. But we are back in the lab. So I have master’s students that will be doing studies in the lab, at least so far, so good, but Norway has a very low COVID-19 exposure right now, Canada, still labs are closed, as far as I know. And I’m working with a couple of PhD students from Canada that had laboratory studies planned as part of their PhD work in that in a, you know, they’ve had to jump through hurdles and try to figure out how to re think their whole PhD sequence of studies and add a crowdsourced component to their, their research program. So, you know, this has been this has been tough for a lot of PhD students, particularly there, we’re kind of in the middle of some planned sequence of projects where the lat you know, laboratories were vital, they are either sitting and waiting, or they are trying to figure out a different, you know, a plan B, we’re gonna get back in the lab, I do think, but I don’t think it’ll ever be exactly the same in the sense that I do think that we will see hybrid, more hybrid approaches where we can use these two methods in a symbiotic way. I think that’s the best case scenario is not to see not to put them up against each other. So I do labs that Oh, no, I do crowdsource research and, and then start arguing about the pros and cons of each. But we instead say, yeah, the lab study gives us this and this advantage, we can be more invasive, we can be more mechanistically oriented, really tight control. And then the other, the crowd sourced approach gives us real world translate ability, it gives us big numbers, it gives us individual variation, and so forth. And when we put those together, we’ve got a pretty strong tool, a pretty strong conceptual approach to strengthening our understanding of the training process. That’s, that’s the way I see it, but it’s gonna evolve, and we’re gonna make mistakes. So does that. What do you think about that? As a consumer? of research?
Chris Case 56:59
Absolutely. Yeah, I anticipated that answer.
Dr. Stephen Seiler 57:04
You know, me too well, taking
Chris Case 57:05
these two things in combination is, you know, I like the fact that you would want to see the positives of each rather than pitting one of these against the other and only seeing the pros and cons, but each has its advantages, so why not use both.
Dr. Stephen Seiler 57:23
And then even got some hybrid solutions, if you go to inside and Sebastian, wherever, which is, you know, that’s it’s a company, it’s a private issue, but people can pay to, you know, kind of a hybrid laboratory situation where they do the lactate measurements, they’ve got the bike with the power, and so they do the protocol that they’re told to do by the company. And then the company analyzes their data, and gives them sophisticated analytics of these different variables that inside, tries to extract from the power and like to connect data and so forth. So this is this hybrid approach is probably also be will end up being used more also in a kind of a, which we say, is a business. So that’ll be you know, that’s an interesting development as well. And I’ll my problem with all this stuff, or is I hate black boxes. And so I’m always skeptical to any kind of algorithms that are behind a paywall, or inside a black box, in a sense, so but But I get it, because that’s how people make money from what would otherwise be just quickly spread in every direction is to create some kind of paywall or black box algorithm. But for us science purists we don’t trust anything that we can’t see exactly where the numbers come from, anyways, but but it makes for good discussions about these different metrics and about what they mean what they don’t mean, and so forth. So that’s, that’s some of the stuff I you know, I see is a backdrop for some future discussions, we’re going to have maybe with different people, different scientists, different users of the science and producers of the science. You know, this, I hope, I would like to think that I try to be a bridge builder between people listening, who are the consumers, meaning coaches, athletes, and so forth. And the sports science community, many of us are also avid sports followers and practitioners, but we get paid to do research into provide meaningful information that can be translated into better practice. So that’s kind of some of the stuff that I like to talk about. And obviously, we’re all being affected by this kind of this development. This this technological development, this digital footprint. We’re all leaving, and what can we use it for? And pros and cons of different approaches. So that’s the goal. Did I forget to say anything? Chris, are there other questions you have?
Chris Case 1:00:13
No, I think that was a great, somewhat history lesson. And also just showing that arc of where we’ve come from and where we’re at now. And where we hope to go sets the stage for, as you said, future conversations where we can talk about how do you take all of this great information and put it into practice? If you’re a consumer of this stuff? How do you make it most accessible if you’re the creator of some of this research? So I think those are two great future questions to answer on upcoming episodes.
Dr. Stephen Seiler 1:00:49
Good. Well, then, I think I will. It’s Friday night here in Norway.
Chris Case 1:00:55
Time to party.
Dr. Stephen Seiler 1:00:56
As I’m speaking, you know me I am a party animal. So you know, it’s it. Well, good grief. It’s already six o’clock here, so 6 PM, time to party.
Rob Pickels 1:01:09
That was another episode of Fast Talk. Subscribe to fast talk wherever you prefer to find your favorite podcast. Be sure to leave us a rating, and a review. The thoughts and opinions expressed on Fast Talk are those of the individual. As always, we love your feedback. Join the conversation at forums@fastalklabs.com to discuss each and every episode, become a member of that stock laboratories at fasttalklabs.com/join. To become a part of our education and coaching community for Chris Case and Dr. Stephen Seiler, and Trevor Connor, I’m Rob Pickels. Thanks for listening!
